Methods, devices, and computer equipment for predicting vehicle parameters

Abstract

This application discloses a method, apparatus, and computer device for predicting vehicle parameters, belonging to the field of automotive technology. The method includes: acquiring a dynamic model corresponding to the vehicle; acquiring the vehicle's state variables, control variables, and output variables from the base dynamic model; constructing a baseline prediction model for the vehicle based on the state variables, control variables, and output variables; acquiring the vehicle's disturbance variables; constructing a target prediction model for the vehicle based on the state variables, control variables, output variables, and disturbance variables; and constructing a vehicle parameter prediction model based on the difference between the baseline prediction model and the target prediction model. This improves the controllability and comfort of vehicle control.

Description

Technical Field

[0001] This application relates to the field of vehicle-mounted systems, and in particular to a method, apparatus, and computer device for predicting vehicle parameters. Background Technology

[0002] As vehicles become increasingly intelligent, their chassis control systems, including active steering and active suspension, are used to ensure vehicle stability during autonomous driving.

[0003] In related technologies, the vehicle's chassis system integrates an active suspension controller. This controller determines the additional force the active suspension should apply based on the vehicle's driving information to ensure vehicle stability under different road conditions. For example, when the vehicle travels over a raised surface, the active suspension applies additional upward force to help the vehicle overcome the raised surface.

[0004] However, during the autonomous driving process, the external environment and road conditions that the vehicle has to deal with are uncontrollable. The active suspension controller focuses on the longitudinal movement of the vehicle. After the active suspension applies additional force, although it can ensure the controllability of the vehicle suspension, suddenly applying force to the suspension is unstable for the vehicle body. Summary of the Invention

[0005] This application provides a method, apparatus, and computer equipment for predicting vehicle parameters, thereby improving the controllability and comfort of vehicle control. The technical solution is as follows:

[0006] According to one aspect of this application, a method for predicting vehicle parameters is provided, the method comprising:

[0007] Obtain the dynamic model corresponding to the vehicle, which includes a steering model, a suspension model, and a tire model. The dynamic model is used to construct the dynamic relationship between the vehicle's driving state and physical characteristics.

[0008] Based on the dynamic model, the state variables, control variables and output variables corresponding to the vehicle are obtained. The state variables include a first parameter related to the driving state and the physical characteristics. The control variables include a second parameter related to the front wheel steering angle and the suspension power. The output variables include a third parameter corresponding to the yaw angle of the vehicle and the path displacement of the vehicle in the lateral direction.

[0009] Based on the state variables, the control variables, and the output variables, a baseline prediction model corresponding to the vehicle is constructed.

[0010] Obtain the disturbance variables corresponding to the vehicle, wherein the disturbance variables are used to represent environmental variables that cause state disturbances to the driving state of the vehicle;

[0011] Based on the state variables, control variables, output variables, and disturbance variables, a target prediction model corresponding to the vehicle is constructed.

[0012] Based on the difference between the baseline prediction model and the target prediction model, a vehicle parameter prediction model is constructed. The vehicle parameter prediction model is used to predict the force of the vehicle suspension when the vehicle undergoes displacement changes.

[0013] According to one aspect of this application, a vehicle parameter prediction device is provided, the device comprising:

[0014] The acquisition module is used to acquire the dynamic model corresponding to the vehicle. The dynamic model includes a steering model, a suspension model, and a tire model. The dynamic model is used to construct the dynamic relationship between the vehicle's driving state and physical characteristics.

[0015] The acquisition module is further configured to acquire the state variables, control variables and output variables corresponding to the vehicle based on the dynamic model. The state variables include a first parameter related to the driving state and the physical characteristics. The control variables include a second parameter related to the front wheel steering angle and the suspension power. The output variables include a third parameter corresponding to the yaw angle of the vehicle and the path displacement of the vehicle in the lateral direction.

[0016] A construction module is used to construct a baseline prediction model for the vehicle based on the state variables, the control variables, and the output variables.

[0017] The acquisition module is also used to acquire the disturbance variable corresponding to the vehicle, and the disturbance variable is used to represent the environmental variable that causes state disturbance to the driving state of the vehicle.

[0018] The construction module is used to construct a target prediction model corresponding to the vehicle based on the state variable, the control variable, the output variable, and the disturbance variable.

[0019] The construction module is used to construct a vehicle parameter prediction model based on the difference between the benchmark prediction model and the target prediction model. The vehicle parameter prediction model is used to predict the force of the vehicle suspension when the vehicle undergoes displacement changes.

[0020] According to one aspect of this application, a computer device is provided, the computer device including a processor and a memory, the memory storing a computer program, the processor loading and executing the computer program to implement the above-described method for predicting vehicle parameters.

[0021] According to another aspect of this application, a computer-readable storage medium is provided, which stores a computer program that is loaded and executed by a processor to implement the above-described method for predicting vehicle parameters.

[0022] According to another aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the aforementioned method for predicting vehicle parameters.

[0023] The beneficial effects of the technical solutions provided in this application include at least the following:

[0024] A dynamic model is formed by coupling the vehicle's steering system, suspension system, and tire model. Variables corresponding to the front wheel steering angle and lateral path displacement are introduced into the steering system to construct a baseline prediction model for the vehicle. Then, road excitation conditions during actual vehicle operation are simulated to generate disturbance variables. A target prediction model is generated based on these disturbance variables and the three variables (state variable, output variable, and control variable) corresponding to the dynamic model. Finally, a vehicle parameter prediction model is constructed based on the differences between the target prediction model and the baseline prediction model. In practical applications, the in-vehicle intelligent driving system incorporates this vehicle parameter prediction model. During vehicle operation, it predicts the suspension forces corresponding to changes in vehicle displacement based on information such as vehicle speed and applies the corresponding suspension forces accordingly, further improving the vehicle's operational stability. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of a method for predicting vehicle parameters provided in an exemplary embodiment of this application;

[0027] Figure 2 This is a flowchart of a method for predicting vehicle parameters provided in an exemplary embodiment of this application;

[0028] Figure 3 This is a schematic diagram of a steering model provided in an exemplary embodiment of this application;

[0029] Figure 4 This is a schematic diagram corresponding to the side view of a suspension model provided in an exemplary embodiment of this application;

[0030] Figure 5 This is a schematic diagram corresponding to the front view of a suspension model provided in an exemplary embodiment of this application;

[0031] Figure 6 This is a flowchart of a method for predicting vehicle parameters provided in another exemplary embodiment of this application;

[0032] Figure 7 This is a flowchart of a method for predicting vehicle parameters provided in another exemplary embodiment of this application;

[0033] Figure 8 This is a flowchart of a vehicle parameter prediction device provided in an exemplary embodiment of this application;

[0034] Figure 9 This is a flowchart of a vehicle parameter prediction device provided in another exemplary embodiment of this application;

[0035] Figure 10 This is a structural block diagram of a computer device provided in an exemplary embodiment of this application;

[0036] Figure 11 This is a structural block diagram of a server provided in an exemplary embodiment of this application. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0038] The vehicle parameter prediction method provided in this application embodiment can be implemented by the vehicle alone, by the server alone, or by both the server and the vehicle.

[0039] When this method is implemented by the vehicle alone, the model / computation program related to the vehicle parameter prediction method is integrated into the vehicle's intelligent driving system. During the vehicle's autonomous driving process, the above-mentioned vehicle parameter prediction method is implemented through the intelligent driving system.

[0040] This explanation will take the example of a method implemented jointly by a server and a vehicle. Figure 1 An execution structure block diagram of a vehicle parameter prediction method provided by an exemplary embodiment is shown. The method is illustrated using an example of its application in server 100.

[0041] Optionally, the server 100 acquires the physical features corresponding to the vehicle in the driving state. The physical features are used to characterize the driving parameters (such as driving acceleration), tire parameters (such as steering angles corresponding to the four tires), and suspension parameters (such as stiffness of the active suspension in the vehicle) of the vehicle during the driving process.

[0042] Server 100 generates a dynamic model of the vehicle based on physical characteristics. This dynamic model is used to construct the dynamic relationship between the vehicle's driving state and its physical characteristics. This allows relevant personnel to more intuitively understand the parameters corresponding to the vehicle's steering, active suspension, and tires.

[0043] Server 100 categorizes all physical characteristics into state variables, control variables, and output variables based on a dynamic model.

[0044] Server 100 constructs a baseline prediction model 101 for the vehicle based on state variables, control variables, and output variables.

[0045] Server 100 receives disturbance variables and combines them with state variables, control variables, and output variables to construct a target prediction model 102 corresponding to the vehicle.

[0046] Server 100 constructs vehicle parameter prediction model 103 based on the differences between the baseline prediction model 101 and the target prediction model 102.

[0047] In the process of applying the vehicle prediction model 103, the server 100 receives the physical features to be predicted related to the control variables during the vehicle's driving process, inputs the physical features to be predicted into the vehicle parameter prediction model 103, and the vehicle parameter prediction model 103 realizes functions such as vehicle path tracking and suspension force prediction.

[0048] The above process only describes the execution process of server 100. In actual applications, server 100 can perform the above data interaction with the terminal or with the vehicle, and this application does not limit this. The terminal can be any intelligent device with data interaction capabilities.

[0049] It is worth noting that when terminal 10 is implemented as a smart terminal, the smart terminal can be: a smartphone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 player (Moving Picture Experts Group Audio Layer IV), a laptop computer, or a desktop computer. The terminal can also be referred to as user equipment, a portable terminal, a laptop terminal, a desktop terminal, or other names, and this application does not limit the specific name used.

[0050] Server 100 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, Server 100 can also be implemented as a node in a blockchain system.

[0051] In this embodiment, the vehicle parameter prediction model 103 is integrated into the intelligent driving system within the vehicle. When the vehicle is in motion, the intelligent driving system directly acquires the physical features to be predicted based on the features generated during the driving process, inputs these physical features into the vehicle prediction model 103, and outputs path tracking results and suspension forces.

[0052] It should be noted that all information (including but not limited to vehicle information), data (including but not limited to data used for analysis, data stored, data displayed), and signals involved in this application are authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions.

[0053] like Figure 2 As shown, Figure 2 A flowchart illustrating the execution of a vehicle parameter prediction method provided in an exemplary embodiment of this application is shown. The method is described using a server as an example.

[0054] Step 201: Obtain the dynamic model corresponding to the vehicle.

[0055] Among them, the dynamic model is used to construct the dynamic relationship between the vehicle's driving state and physical characteristics.

[0056] During vehicle operation, the intelligent driving system inside the vehicle acquires the physical characteristics of the vehicle. These physical characteristics are used to characterize the driving parameters of the vehicle, the tire parameters of the vehicle's tires, and the suspension parameters of the vehicle's suspension.

[0057] The driving parameters include, but are not limited to, at least one of the following during the vehicle's driving process: lateral speed, longitudinal speed, yaw angle, yaw rate, acceleration, lateral displacement, and longitudinal displacement.

[0058] Tire parameters are used to indicate the force parameters corresponding to the tires on the vehicle body. Tire parameters include, but are not limited to, the lateral force corresponding to the tires on the vehicle body.

[0059] Suspension parameters indicate the parameters of the elastic elements within the vehicle's suspension system in the lateral (to the left of the vehicle's center of gravity), longitudinal (to the front of the vehicle's center of gravity), and vertical (perpendicular to the vehicle's center of gravity) directions. Suspension parameters include, but are not limited to, at least one of the following: body roll moment of inertia, pitch moment of inertia, and vertical moment of inertia.

[0060] In this embodiment of the application, the dynamic model includes a steering model, a suspension model, and a tire model.

[0061] The steering model is established based on the following physical characteristics, including vehicle mass m and sprung mass (the total mass supported by the elastic elements in the vehicle suspension system). s longitudinal speed of the vehicle v x lateral speed of the vehicle v y Yaw rate (ω, the speed at which the vehicle rotates around a vertical direction during rotation), and lateral force S corresponding to each of the four tires (the lateral reaction force exerted by the ground on the tires). i (where i = 1, 2, 3, 4), the height h of the center of mass above the ground, and the front wheel steering angle (the maximum angle the front wheel can reach when steering) δ f The yaw moment of inertia of a vehicle (referring to the moment of inertia of a vehicle rotating about its vertical axis) I z a) Distance from center of gravity to front wheel; b) Distance from center of gravity to rear wheel; φ) Body roll angle (the angle of inclination of the vehicle body relative to the vertical direction when the vehicle is rotating or traveling on an uneven road surface); yaw angle (the difference between the vehicle's heading angle and the sideslip angle). The vehicle's longitudinal position (Y) and lateral position (X) in the vehicle's global coordinate system, and the longitudinal lateral stiffness (C) of the vehicle's front tires (referring to the tire's stiffness characteristics during longitudinal slippage). lf The longitudinal lateral stiffness C of the vehicle's rear tires lr The slip ratio of the front wheels (referring to the proportion of slippage during tire movement) S f And the slip ratio S of the rear wheels r .

[0062] In the embodiments of this application, the steering model can be referred to Formulas 1 to 5 below.

[0063] Formula 1:

[0064] Formula 2:

[0065] Formula 3:

[0066] Formula 4:

[0067] Formula 5:

[0068] The meanings of the parameters appearing in formulas 1-5 above can be found in the above content. Among them, This refers to the lateral vehicle speed v y Find the first derivative. This refers to the longitudinal vehicle speed v x Find the first derivative. This refers to taking the second derivative of the vehicle body roll angle. This refers to taking the first derivative with respect to the yaw rate ω. This refers to taking the first derivative with respect to the vertical position Y. This refers to taking the first derivative with respect to the horizontal position X.

[0069] Construct a suspension model using the following physical characteristics. These physical characteristics include the vehicle's roll moment of inertia (the moment of inertia when the vehicle rotates about its lateral direction). x Pitch moment of inertia (referring to the moment of inertia I of a vehicle rotating about its longitudinal axis) y The total force F exerted by the suspension on the vehicle body at each wheel. i (where i = 1, 2, 3, 4), constant d, vertical displacement z at the vehicle's center of mass. s The variables are gravitational acceleration g and vehicle pitch angle θ (the height difference between the front and rear positions of the vehicle). In this embodiment, d is half the track width.

[0070] In this embodiment of the application, the suspension model includes the pitch motion equation corresponding to the sprung mass, the roll motion equation corresponding to the sprung mass, the vertical motion equation corresponding to the sprung mass, the vertical motion equation of the unsprung mass, the force equation of the suspension on the vehicle body, and the displacement corresponding to the four tires of the vehicle body.

[0071] The equations for pitch motion can be found in Formula 6 below, the equations for roll motion can be found in Formula 7 below, and the equations for vertical motion can be found in Formula 8 below.

[0072] Formula 6:

[0073] Formula 7:

[0074] Formula 8:

[0075] In the above formulas 6-8, the parameters are marked with points and double points, which represent the first and second derivatives of the parameter, respectively.

[0076] The vertical motion equation of the unsuspended mass can be expressed as shown in Formula 9 below.

[0077] Formula 9:

[0078] In formula 9 above, m ui For the mass portions corresponding to the four elastic elements of the vehicle (i = 1, 2, 3, 4), k t This refers to the vertical stiffness of the four tires, z-axis. gi This refers to the vertical displacement of the road surface (i = 1, 2, 3, 4), z ui This refers to the vertical displacement corresponding to the four elastic elements of the vehicle.

[0079] Based on the above formula, and combined with the damping coefficients c of the four suspensions... i (i = 1, 2, 3, 4), the suspension stiffness k corresponding to the four suspensions. si (i = 1, 2, 3, 4), Front suspension lateral stabilizer bar angular stiffness k af 1. Rear suspension stabilizer bar angular stiffness k ar And the vertical displacement z at the four suspension connections to the body. si (i = 1, 2, 3, 4) Determine F i The force equations are shown in Formula 10-13 below.

[0080] Formula 10:

[0081] Formula 11:

[0082] Formula 12:

[0083] Formula 13:

[0084] The parameters in formulas 11-13 above can be found in the textual descriptions above.

[0085] In this embodiment of the application, when similar values ​​exist within a preset range for the pitch angle θ, the vertical displacement z at the vehicle's center of gravity can be directly utilized. sThe pitch angle θ, the distance a from the center of gravity to the front wheel, the distance b from the center of gravity to the rear wheel, and the roll angle φ directly determine the vertical displacement z at the connection points between the four suspensions and the vehicle body. si For details, please refer to formulas 14-17 below.

[0086] Formula 14: z s1 =z s -aθ-dφ;

[0087] Formula 15: z s2 =z s -aθ+dφ;

[0088] Formula 16: z s3 =z s +bθ+d;

[0089] Formula 17: z s4 =z s +bθ-dφ;

[0090] The parameters in formulas 14-17 above can be found in the text description above.

[0091] Construct a tire model using the following physical characteristics. Optionally, use the lateral force S corresponding to the four tires. i (where i = 1, 2, 3, 4) is used to characterize the tire model, and can be found in formulas 18-19 below.

[0092] Formula 18:

[0093] Formula 19:

[0094] In Formulas 18-19, k1 and k2 are the lateral stiffness of the front and rear wheels, respectively, and α1 and α2 are the lateral slip angles of the front and rear wheels, respectively. Other parameters can be found in the above content.

[0095] Combining the three models mentioned above and Figure 3 A vehicle steering model for vehicle 300 in the vehicle global coordinate system is established. The vehicle steering model includes a vehicle coordinate system with the center of mass of vehicle 300 as the origin, the x-axis pointing forward of vehicle 300, and the y-axis pointing to the left of vehicle 300. Vehicle 300 includes tires 1-4. The parameters of vehicle 300 under the specified steering trajectory 301 are shown in the figure. These parameters are described in the text above and will not be repeated here.

[0096] like Figure 4 As shown, Figure 4 The side view of the vehicle suspension model 400 corresponding to the vehicle is shown. The various parameters appearing in the vehicle suspension model 400 are also as described in the above text, and will not be repeated here.

[0097] like Figure 5 As shown, Figure 5 The front view of the vehicle suspension model 500 corresponding to the vehicle is shown. The various parameters appearing in the vehicle suspension model 500 are also as described in the above text, and will not be repeated here.

[0098] Step 202: Obtain the state variables, control variables, and output variables corresponding to the vehicle based on the dynamic model.

[0099] Optionally, based on the above, the physical characteristics contained in the dynamic model are divided into state variables, control variables, and output variables.

[0100] The state variables include a first parameter related to the driving state and the physical characteristics, the control variables include a second parameter related to the front wheel steering angle and the suspension power, and the output variables include a third parameter corresponding to the yaw angle of the vehicle and the path displacement of the vehicle in the lateral direction.

[0101] Optionally, the first parameter (state variable) includes v y v x , ω, X, θ, z s , Y, φ, z ui , At least one of the following. Where i takes the value 1, 2, 3, or 4.

[0102] In this embodiment of the application, the first parameter (state variable) includes the above-mentioned x y v x , ω, X, θ, z s , Y, φ, z ui as well as

[0103] The second parameter (control variable) includes the front wheel steering angle and the corresponding actuation force of the four suspension actuators; that is, the second parameter includes δ and f. i At least one of the following. Where i takes the value 1, 2, 3, or 4.

[0104] In this embodiment, the second parameter (control variable) includes δ and f. i Five parameters.

[0105] The third parameter (output variable) includes Y、 θ, φ, z s1 -z u1 z s2 -z u2 zs3 -z u3 z s4 -z u4 At least one of them.

[0106] In this embodiment of the application, the third parameter (output variable) includes Y、 θ, φ, z s1 -z u1 z s2 -z u2 z s3 -z u3 and z s4 -z u4 .

[0107] Step 203: Based on the state variables, control variables, and output variables, construct the benchmark prediction model corresponding to the vehicle.

[0108] Optionally, an initial prediction model is obtained by inputting the input features corresponding to the state variables, control variables, and output variables into the initial prediction model to obtain a first candidate prediction result. The first candidate prediction result is compared with the theoretical prediction result corresponding to the input features to obtain a difference parameter; the initial prediction model is adjusted based on the difference parameter to obtain a baseline prediction model.

[0109] In another optional embodiment, a nominal equation corresponding to the vehicle is determined based on the state variables, control variables, and output variables, and a baseline prediction model is constructed based on this nominal equation. The nominal equation is used to represent the correspondence between the state variables, control variables, and output variables.

[0110] For illustrative purposes, the nominal equations can be found in Equations 20-21 below.

[0111] Formula 20: x new =Ax + Bu;

[0112] Formula 21: y = Cx + Du;

[0113] In formulas 20-21 above, x new Let u represent the first parameter, y represent the second parameter, and A, B, C, and D represent matrices of preset dimensions.

[0114] A baseline prediction model is constructed based on the above nominal equation.

[0115] The nominal equation above is linearized to obtain a linear nominal equation. Specifically, the nominal equation is linearized based on the dimension n of the state variables and the dimension m of the output variables. The specific linearization solution process can be found in Formula 22 below.

[0116] Formula 22:

[0117] In formula 22 above, This refers to taking the derivative of the first variable in the output variables and the first variable in the state variables. The ratios in the rest of Formula 22 are calculated using the same method.

[0118] Optionally, determine the objective function corresponding to the control variables and output variables. The objective function is used to characterize the relationship between the longitudinal position Y and the influence of the control variables.

[0119] Specifically, the ideal path displacement of the vehicle is determined based on the initial state model; the objective function is determined based on the ideal path displacement and the control variables.

[0120] Schematic, the objective function is based on the vehicle's actual longitudinal position Y and ideal longitudinal position. And the control variables are determined.

[0121] In the embodiments of this application, Y in the above objective function and This refers to all parameters within the output variables mentioned above.

[0122] In an optional embodiment, the linear nominal equation is discretized to obtain a discrete state equation; a rolling prediction process is performed on the discrete state equation to obtain a rolling state equation; and the objective function is determined based on the rolling state equation. Here, the discretization process refers to using the current state variables and output variables to determine the state variables and control variables corresponding to the vehicle at the next moment. The rolling prediction process refers to continuously updating the prediction results as new physical features emerge.

[0123] To illustrate, the above formulas 20-21 are discretized to obtain the following formulas 23-24, which are discrete state equations.

[0124] Formula 23: x(k+1)=A k x(k)+B k u(k);

[0125] Formula 24: y(k)=C k x(k)+D k u(k);

[0126] In formulas 23-24 above, k is an integer greater than 0. When k takes different values, the parameters A, B, C, and D in formulas 23-24 also correspond to different values. k can also be understood as the number of iterations of the state variables, control variables, and output variables during the discretization process.

[0127] In an optional embodiment, after obtaining the discretized equations, the transformed control variables are generated. Illustratively, the expression corresponding to the transformed control variables is ξ(k|t)=[x(k)u(k-1)] T , where k is the number of iterations, t is the current time, and T refers to the transpose operation.

[0128] The transformation nominal equation is generated based on the transformation control variables. The transformation nominal equation can be found in formulas 25-26 below.

[0129] Formula 25:

[0130] Formula 26:

[0131] In the above formulas 25-26, Δu(k)=[Δδf1f2f3f4], where Δδ refers to the change in the front wheel steering angle within the target time period. This is used to represent the matrix values ​​corresponding to the above matrices A, B, and C during the k-th iteration.

[0132] Rolling prediction is performed on the discrete state equations corresponding to Equations 20-21 to obtain the rolling state equations. See Equation 27 below for details.

[0133] Formula 27:

[0134] In formula 27 above:

[0135] Np represents the prediction time domain, and Nc represents the control time domain.

[0136] Determine the objective function. Specifically, determine the ideal path displacement of the vehicle based on the initial state model; and determine the objective function based on the ideal path displacement and control variables.

[0137] In an optional embodiment, the actual longitudinal position Y and the ideal longitudinal position of the vehicle are obtained. In addition to the control variables, a target function is generated based on these three parameters. The target function can be found in Formula 28 below. All parameters appearing in Formula 28 have been mentioned in the above text and will not be repeated here.

[0138] Formula 28:

[0139] In formula 28 above, Q Q For the weights of the output variables, R R To control the weights of variables.

[0140] Optionally, the minimum value corresponding to the control variable within the objective function is determined; based on the minimum value and the linear nominal equation, the initial prediction model is adjusted to obtain the baseline prediction model.

[0141] The process of determining the minimum value is as follows: First, determine the constraints corresponding to the front wheel steering angle, which indicate the range of values ​​for that angle. Then, based on these constraints, determine the minimum value of the control variable within the objective function using a pre-defined solution method. The pre-defined solution method is quadratic programming.

[0142] In this embodiment, constraints are imposed on the front wheel steering angle and driving force within the control variables. That is, the constraints corresponding to the front wheel steering angle and driving force are determined, and these constraints are decided by relevant personnel based on the actual conditions of the vehicle.

[0143] In this embodiment, the constraints include the range of values ​​corresponding to the front wheel steering angle and the range of values ​​for the applied force. Front wheel steering angle δ f The range is [-3.5rad ≤ δ] f ≤3.5rad], front wheel steering angle change Δδ f The range is [-0.35rad ≤ Δδ] f ≤3.5rad]. The range of the driving force f is [-5000N≤f≤5000N].

[0144] Use the following formula 29 to determine the minimum value corresponding to the increment of the control variable.

[0145] Formula 29:

[0146] In formula 29 above, Among them, the control variable and the control variable increment conform to the preset relationship u(k+o)=u(k+o-1)+Δu(k), where o is an integer greater than 0.

[0147] Optionally, a quadratic programming solution method can be used to determine the minimum value corresponding to the increment of the control variables. Illustratively, the quadprog QP solver provided by the matrix factory (i.e., Matlab software) can be used to solve the objective function to obtain the minimum value.

[0148] Based on the minimum value and the rolling state equation, the initial prediction model is adjusted to obtain the baseline prediction model.

[0149] Step 204: Obtain the disturbance variables corresponding to the vehicle.

[0150] Optionally, the disturbance variable is used to represent the environmental variables that cause state disturbances to the vehicle's driving state. The disturbance variable can be regarded as the road excitation during the vehicle's driving process. Road excitation refers to the dynamic excitation of the vehicle caused by road unevenness during driving, which affects the vehicle's vibration, comfort, safety, and corresponding dynamic performance.

[0151] In this embodiment, the road surface excitation (disturbance variable) consists of random unevenness and discrete events. Random unevenness refers to the uneven cross-section corresponding to the road surface, while discrete events are caused by circular protrusions, bow-shaped protrusions, and wavy road surfaces, etc.

[0152] In another optional embodiment, the disturbance variables also include sub-variables characterizing environmental information, such as wind parameters, temperature parameters, and humidity parameters.

[0153] As can be seen from the above, the disturbance variables include variables corresponding to random unevenness, variables corresponding to discrete events, and sub-variables.

[0154] Step 205: Based on the state variables, control variables, output variables, and disturbance variables, construct the target prediction model corresponding to the vehicle.

[0155] Optionally, an initial prediction model is obtained. The input features corresponding to the disturbance variable, state variable, control variable, and output variable are input into the initial prediction model to obtain candidate prediction results. The candidate prediction results are compared with the theoretical prediction results corresponding to the input features to obtain a difference parameter; the initial prediction model is adjusted based on this difference parameter to obtain the target prediction model.

[0156] Step 206: Based on the differences between the baseline prediction model and the target prediction model, construct a vehicle parameter prediction model.

[0157] Optionally, the data to be predicted is acquired, and simultaneously input into both the benchmark prediction model and the target prediction model to obtain a first result corresponding to the benchmark prediction model and a second result corresponding to the target prediction model. The first and second results are then fitted to obtain a third result.

[0158] The third result is used as input to the target prediction model to obtain the fourth result. Based on the difference between the third and fourth results, the model parameters within the target prediction model are corrected to obtain the vehicle parameter prediction model.

[0159] In another optional embodiment, the baseline prediction model and the target prediction model are coupled to obtain a vehicle parameter prediction model. The coupling process includes establishing an initial coupled model, determining the excitation input, simulation analysis, parameter influence analysis, and verification and optimization.

[0160] Establishing the initial coupled model refers to creating a dynamic model of the vehicle and a dynamic model of the road surface under the condition of vehicle-road coupling, and determining the interaction between the two models. Illustratively, the vehicle and road are linked through the displacement compatibility condition at the tire-road contact point, thereby establishing the initial coupled model corresponding to the vehicle-road coupled system.

[0161] Determining the excitation input means using the road surface excitation as the input to the vehicle response in the coupled initial model to simulate the road surface roughness. Typically, a Gaussian stationary stochastic process is used to simulate the road surface roughness, generating a sequence of inequality at different road surface roughness levels, which is presented in matrix form.

[0162] Simulation analysis refers to using simulation tools to simulate a coupled initial model. During the simulation, the inequality sequence corresponding to the road surface excitation is used as input to analyze the vehicle's response to the road surface excitation.

[0163] Parametric influence analysis refers to analyzing the impact of different physical characteristics on the coupled initial system. This includes the effects of vehicle speed variation, changes in road surface inequality on vehicle dynamic load coefficient and vertical acceleration, and the influence of different road base stiffness on vehicle vibration characteristics.

[0164] The verification and optimization process refers to verifying the accuracy and reliability of the coupled initial model through experiments and actual tests. Based on the verification results, the coupled initial model is optimized to obtain the above-mentioned vehicle parameter prediction model.

[0165] Among them, the vehicle parameter prediction model is used to realize functions such as path tracking and prediction of suspension action.

[0166] Among them, path tracking refers to the displacement information of the vehicle in the current driving state corresponding to the future moment, and the predicted suspension action refers to the prediction of the pressure exerted by the four suspensions of the vehicle when facing the road conditions in the future moment.

[0167] This method illustrates how the vehicle's current speed and road surface unevenness level are input into a vehicle parameter prediction model to predict a series of physical characteristics of the vehicle in the future, including its lateral position, longitudinal position, yaw angle, pitch angle, and roll angle. Based on the predicted results corresponding to the suspension-related physical parameters, the model applies additional force to the suspension to ensure ride comfort under various road conditions. Furthermore, the model predicts the corresponding active front wheel steering angle to ensure the vehicle's ability to track its path.

[0168] In this embodiment, the vehicle's steering system, suspension system, and tire model are coupled to form a dynamic model. Variables corresponding to the front wheel steering angle and lateral path displacement are introduced into the steering system to construct a baseline prediction model for the vehicle. Then, road excitation conditions during actual vehicle operation are simulated to generate disturbance variables. A target prediction model is generated based on the disturbance variables and three variables (state variable, output variable, and control variable) corresponding to the dynamic model. Finally, a vehicle parameter prediction model is constructed based on the difference between the target prediction model and the baseline prediction model. In practical applications, the in-vehicle intelligent driving system incorporates this vehicle parameter prediction model. During vehicle operation, based on information such as vehicle speed, it predicts the suspension forces corresponding to changes in vehicle displacement and applies the corresponding suspension forces accordingly. This further improves the vehicle's operational stability.

[0169] like Figure 6 As shown, Figure 6 A flowchart illustrating the execution of a vehicle parameter prediction method provided in another exemplary embodiment of this application is shown. The method is described using a server as the execution entity.

[0170] Step 600: Construct a vehicle parameter prediction model.

[0171] Obtain the nominal equations and disturbance variables corresponding to the vehicle. Adjust the nominal equations based on the disturbance variables to determine the actual state equations corresponding to the vehicle.

[0172] Optionally, the perturbation variable can be denoted as w(k).

[0173] Based on the disturbance variable w(k), Equations 23-24 are adjusted to obtain Equations 31-32, which are used to represent the actual nominal equations generated by the vehicle during actual application.

[0174] Formula 31:

[0175] Formula 32:

[0176] In formulas 31-32 above, These are the state vector, control vector, and output vector of the vehicle in actual application, respectively, and w(k) is the disturbance input.

[0177] In another optional embodiment, the error state equation is determined based on the nominal equation and the actual state equation. That is, the error state system is obtained by subtracting the nominal state equation and the actual nominal equation obtained in the above process. in,

[0178] Optionally, a vehicle parameter prediction model can be obtained based on the above error state equation.

[0179] Optionally, determine the error prediction model corresponding to the error state equation; couple the error prediction model and the baseline prediction model to obtain the vehicle parameter prediction model.

[0180] In another alternative embodiment, a linear quadratic regulator (LQR) is used to solve for the minimum values ​​of each variable in the error nominal state system.

[0181] The LQR controller will be explained below.

[0182] The core of the LQR controller is to minimize the objective quadratic cost function, which can be expressed in Equation 33 below.

[0183] Formula 33:

[0184] in, Let Q represent the nominal system model performance output cost, and let Q be the performance weighting matrix. R represents the cost of the vehicle controller, and R is the control weighting matrix. Let S represent the terminal cost, where S is the vehicle weighting matrix, and I and N are positive integers. Used to represent the mean value corresponding to the I-th output variable.

[0185] Determine the feedback gain matrix of the LQR controller. This feedback gain matrix can be determined using Equation 34 below.

[0186] Formula 34:

[0187] In Formula 34 above, N is a positive integer, q is any value from 1 to N, and P is a solution to the Laplace-Markov equation.

[0188] Based on the above objective function, an LQR controller is designed. In this embodiment, the controller is... Where K is the feedback gain matrix of the LQR controller.

[0189] An LQR controller is generated based on state variables and output variables, combined with the feedback gain matrix K.

[0190] Couple the model corresponding to the LQR controller and the baseline prediction model to generate a vehicle parameter prediction model.

[0191] In another optional embodiment, a preset algorithm (here referring to the LRQ control algorithm) is used to solve for the minimum value corresponding to each output variable under the error nominal state system; the error prediction model is trained based on the minimum value to obtain the target error prediction model; the target error prediction model and the benchmark prediction model are coupled to obtain the vehicle parameter prediction model.

[0192] In this embodiment, the vehicle's steering system, suspension system, and tire model are coupled to form a dynamic model. Variables corresponding to the front wheel steering angle and lateral path displacement are introduced into the steering system to construct a baseline prediction model for the vehicle. Then, road excitation conditions during actual vehicle operation are simulated to generate disturbance variables. A target prediction model is generated based on the disturbance variables and three variables (state variable, output variable, and control variable) corresponding to the dynamic model. Finally, a vehicle parameter prediction model is constructed based on the difference between the target prediction model and the baseline prediction model. In practical applications, the in-vehicle intelligent driving system incorporates this vehicle parameter prediction model. During vehicle operation, based on information such as vehicle speed, it predicts the suspension forces corresponding to changes in vehicle displacement and applies the corresponding suspension forces accordingly. This further improves the vehicle's operational stability.

[0193] like Figure 7 As shown, Figure 7 A flowchart illustrating the execution of a vehicle parameter prediction method provided in another exemplary embodiment of this application is shown. The method is described using a server as the execution entity.

[0194] Step 700: Construct the dynamic model.

[0195] Optionally, a steering model, suspension model, and tire model of the vehicle chassis can be created.

[0196] Please refer to step 201 above for the specific execution process of this step, which will not be repeated here.

[0197] Step 701: Couple the dynamic model and linearize the coupled dynamic model.

[0198] Optionally, the three models can be coupled, and the coupled model can be linearized using a Jacobian matrix. This process includes identifying the variables relevant to the nominal equations and linearizing those variables.

[0199] Optionally, state variables, control variables, and output variables are determined based on the dynamic model. These three variables are then linearized into nominal equations using the Jacobian matrix method.

[0200] Please refer to steps 201-202 above for the specific execution process of this step, which will not be repeated here.

[0201] Step 702: Design a predictive controller for the nominal system model.

[0202] Optionally, the nominal equation is determined based on the state variables, control variables, and output variables. The nominal equation is discretized and rolled forecast is performed, and then a suitable objective function and constraints are selected to optimize and solve for the control quantity of the nominal equation.

[0203] The initial prediction model is adjusted using the solved control variables to obtain the model predictive controller.

[0204] Please refer to the steps above for the specific execution process of this step, which will not be repeated here.

[0205] Step 703: Construct the error system and design a target controller for the error system.

[0206] Optionally, a disturbance variable is introduced to design the actual nominal equation. The difference between the nominal equation and the actual nominal equation is taken to obtain the error system. An LQR controller is then designed for the error system using an LQR controller.

[0207] Please refer to step 206 above for the specific execution process of this step, which will not be repeated here.

[0208] Step 704: Couple the model predictive controller and the LQR controller to generate the pipeline model predictive controller.

[0209] Optionally, the two controllers in the design can be coupled to create a pipeline model predictive controller.

[0210] Please refer to step 600 above for the specific execution process of this step, which will not be repeated here.

[0211] In this embodiment, the vehicle's steering system, suspension system, and tire model are coupled to form a dynamic model. Variables corresponding to the front wheel steering angle and lateral path displacement are introduced into the steering system to construct a baseline prediction model for the vehicle. Then, road excitation conditions during actual vehicle operation are simulated to generate disturbance variables. A target prediction model is generated based on the disturbance variables and three variables (state variable, output variable, and control variable) corresponding to the dynamic model. Finally, a vehicle parameter prediction model is constructed based on the difference between the target prediction model and the baseline prediction model. In practical applications, the in-vehicle intelligent driving system incorporates this vehicle parameter prediction model. During vehicle operation, based on information such as vehicle speed, it predicts the suspension forces corresponding to changes in vehicle displacement and applies the corresponding suspension forces accordingly. This further improves the vehicle's operational stability.

[0212] In an optional embodiment, the vehicle parameter prediction method provided above is integrated into the intelligent driving system and presented in the form of a chassis predictive controller. During autonomous driving, the chassis predictive controller acquires the vehicle's corresponding physical characteristics and road surface excitations, controls the vehicle's steering system, and predicts the additional forces applied to the suspension system, thereby achieving robust control of the vehicle chassis. For the active steering system, the front wheel steering angle is output to achieve path tracking control and vehicle stability control. For the active suspension system, the suspension forces output by the chassis predictive controller enable control of vehicle ride comfort. This chassis predictive controller has high robustness, allowing the vehicle to maintain stability control even when faced with external environmental disturbances (such as crosswinds, road surface excitations, and slippery surfaces).

[0213] Figure 8 This application shows a structural block diagram of a vehicle parameter prediction device provided in an exemplary embodiment, the device comprising:

[0214] The acquisition module 800 is used to acquire the dynamic model corresponding to the vehicle. The dynamic model includes a steering model, a suspension model, and a tire model. The dynamic model is used to construct the dynamic relationship between the vehicle's driving state and physical characteristics.

[0215] The acquisition module 800 is further configured to acquire the state variables, control variables and output variables corresponding to the vehicle based on the dynamic model. The state variables include a first parameter related to the driving state and the physical characteristics. The control variables include a second parameter related to the front wheel steering angle and the suspension power. The output variables include a third parameter corresponding to the yaw angle of the vehicle and the path displacement of the vehicle in the lateral direction.

[0216] The construction module 801 is used to construct a baseline prediction model for the vehicle based on the state variables, the control variables, and the output variables.

[0217] The acquisition module 800 is also used to acquire the disturbance variable corresponding to the vehicle, the disturbance variable being used to represent the environmental variable that causes state disturbance to the driving state of the vehicle.

[0218] The construction module 801 is used to construct a target prediction model corresponding to the vehicle based on the state variable, the control variable, the output variable, and the disturbance variable.

[0219] The construction module 801 is used to construct a vehicle parameter prediction model based on the difference between the benchmark prediction model and the target prediction model. The vehicle parameter prediction model is used to predict the force of the vehicle suspension when the vehicle undergoes displacement changes.

[0220] In an optional embodiment, such as Figure 9 As shown, the device further includes:

[0221] The determining module 802 is used to determine the nominal equation corresponding to the vehicle based on the state variable, the control variable, and the output variable, wherein the nominal equation is used to represent the correspondence between the state variable, the control variable, and the output variable;

[0222] The linearization processing module 803 is used to perform linearization processing on the nominal equation to obtain a linear nominal equation;

[0223] The determining module 802 is further configured to determine the objective function corresponding to the control variable and the output variable;

[0224] The determining module 802 is further configured to determine the minimum value corresponding to the control variable within the objective function;

[0225] The determining module 802 is further configured to adjust the initial prediction model based on the minimum value and the linear nominal equation to obtain the benchmark prediction model.

[0226] In an optional embodiment, such as Figure 9 As shown, the device further includes:

[0227] Discretization module 804 is used to perform discretization processing on the linear nominal equation to obtain discrete state equation;

[0228] Prediction module 805 is used to perform a rolling prediction process on the discrete state equation to obtain a rolling state equation;

[0229] The determining module 802 is further configured to adjust the initial prediction model based on the minimum value and the rolling state equation to obtain the benchmark prediction model.

[0230] In an optional embodiment, such as Figure 9 As shown, the determining module 802 is further configured to determine the ideal path displacement of the vehicle based on the initial state model;

[0231] The determining module 802 is further configured to determine the objective function based on the ideal path displacement and control variables.

[0232] In an optional embodiment, such as Figure 9 As shown, the determining module 802 is further used to determine the constraint conditions corresponding to the front wheel steering angle, and the constraint conditions are used to indicate the value range corresponding to the front wheel steering angle;

[0233] The determining module 802 is further configured to determine the minimum value corresponding to the control variable within the objective function based on the constraints and using a preset solution method.

[0234] In an optional embodiment, such as Figure 9 As shown, the acquisition module 800 is also used to acquire the nominal equation corresponding to the vehicle and the disturbance variable;

[0235] The determining module 802 is further configured to adjust the nominal equation based on the disturbance variable to obtain the actual state equation;

[0236] The determining module 802 is further configured to determine the error state equation based on the nominal equation and the actual state equation;

[0237] The determining module 802 is further configured to determine the vehicle parameter prediction model based on the error state equation.

[0238] In an optional embodiment, such as Figure 9 As shown, the determining module 802 is also used to determine the error prediction model corresponding to the determined error state equation;

[0239] The coupling module 806 is used to couple the error prediction model and the baseline prediction model to obtain the vehicle parameter prediction model.

[0240] In an optional embodiment, such as Figure 9 The determining module 802 is further configured to use a preset algorithm to solve for the minimum value corresponding to each output variable under the error nominal state system;

[0241] The determining module 802 is further configured to train the error prediction model based on the minimum value to obtain the target error prediction model;

[0242] The coupling module 806 is also used to couple the target error prediction model and the benchmark prediction model to obtain the vehicle parameter prediction model.

[0243] In this embodiment, the vehicle's steering system, suspension system, and tire model are coupled to form a dynamic model. Variables corresponding to the front wheel steering angle and lateral path displacement are introduced into the steering system to construct a baseline prediction model for the vehicle. Then, road excitation conditions during actual vehicle operation are simulated to generate disturbance variables. A target prediction model is generated based on the disturbance variables and three variables (state variable, output variable, and control variable) corresponding to the dynamic model. Finally, a vehicle parameter prediction model is constructed based on the difference between the target prediction model and the baseline prediction model. In practical applications, the in-vehicle intelligent driving system incorporates this vehicle parameter prediction model. During vehicle operation, based on information such as vehicle speed, it predicts the suspension forces corresponding to changes in vehicle displacement and applies the corresponding suspension forces accordingly. This further improves the vehicle's operational stability.

[0244] Figure 10 This illustration shows a structural block diagram of a computer device 1000 provided in an exemplary embodiment of this application. The computer device 1000 can be a portable mobile terminal, such as a smartphone, tablet computer, MP3 player (Moving Picture Experts Group Audio Layer III), MP4 player (Moving Picture Experts Group Audio Layer IV), laptop computer, or desktop computer. The computer device 1000 may also be referred to as a user device, portable terminal, laptop terminal, desktop terminal, or other names. Optionally, the computer device 1000 can also be implemented as a mobile device, such as a vehicle-mounted terminal or other portable smart terminal.

[0245] Typically, computer device 1000 includes a processor 1001 and a memory 1002.

[0246] Processor 1001 may include one or more processing cores, such as a quad-core processor, an octa-core processor, etc. Processor 801 may be implemented using at least one hardware form selected from DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array). Processor 1001 may also include a main processor and a coprocessor. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, processor 1001 may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, processor 801 may also include an AI (Artificial Intelligence) processor, which is used to handle computational operations related to machine learning.

[0247] The memory 1002 may include one or more computer-readable storage media, which may be non-transitory. The memory 1002 may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory 1002 are used to store at least one instruction, which is executed by the processor 1001 to implement the model training method or behavior encoding method provided in the method embodiments of this application.

[0248] In some embodiments, the computer device 1000 may optionally include a peripheral device interface 1003 and at least one peripheral device. The processor 1001, memory 1002, and peripheral device interface 1003 can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface 1003 via a bus, signal line, or circuit board. For example, the peripheral device may include at least one of the following: a radio frequency circuit 1004, a display screen 1005, a camera assembly 1006, an audio circuit 1007, a positioning assembly 1015, and a power supply 1008.

[0249] Peripheral device interface 1003 can be used to connect at least one I / O (Input / Output) related peripheral device to processor 1001 and memory 1002. In some embodiments, processor 1001, memory 1002 and peripheral device interface 1003 are integrated on the same chip or circuit board; in some other embodiments, any one or two of processor 1001, memory 1002 and peripheral device interface 1003 can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0250] The radio frequency (RF) circuit 1004 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The RF circuit 1004 communicates with communication networks and other communication devices via electromagnetic signals. The RF circuit 1004 converts electrical signals into electromagnetic signals for transmission, or converts received electromagnetic signals back into electrical signals. Optionally, the RF circuit 1004 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a user identity module card, etc. The RF circuit 1004 can communicate with other terminals via at least one wireless communication protocol. This wireless communication protocol includes, but is not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit 1004 may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0251] Display screen 1005 is used to display a UI (User Interface). This UI may include graphics, text, icons, videos, and any combination thereof. When display screen 1005 is a touch display screen, it also has the ability to collect touch signals on or above its surface. These touch signals can be input as control signals to processor 1001 for processing. In this case, display screen 1005 can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, display screen 1005 may be a single screen disposed on the front panel of computer device 1000; in other embodiments, display screen 1005 may be at least two screens, disposed on different surfaces of computer device 1000 or in a folded design; in still other embodiments, display screen 1005 may be a flexible display screen disposed on a curved or folded surface of computer device 1000. Furthermore, display screen 1005 may also be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. The display screen 1005 can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0252] The camera assembly 1006 is used to acquire images or videos. Optionally, the camera assembly 1006 includes a front-facing camera and a rear-facing camera. Typically, the front-facing camera is located on the front panel of the terminal, and the rear-facing camera is located on the back of the terminal. In some embodiments, there are at least two rear-facing cameras, which are any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera, to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly 1006 may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm-light flash and a cool-light flash, which can be used for light compensation at different color temperatures.

[0253] The audio circuit 1007 may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting the sound waves into electrical signals that are input to the processor 1001 for processing, or input to the radio frequency circuit 1004 for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, each located in a different part of the computer device 1000. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor 1001 or the radio frequency circuit 1004 into sound waves. The speaker may be a conventional diaphragm speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuit 1007 may also include a headphone jack.

[0254] The positioning component 1015 is used to calculate the current geographic location of the device 1000 in order to enable navigation or LBS (Location Based Service). The positioning component 1015 can be a positioning component based on the US GPS (Global Positioning System) or the Chinese BeiDou system.

[0255] Power supply 1008 is used to supply power to the various components in computer device 1000. Power supply 1008 can be AC ​​power, DC power, a disposable battery, or a rechargeable battery. When power supply 1008 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged via a wired line, and a wireless rechargeable battery is a battery that is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0256] In some embodiments, the computer device 1000 further includes one or more sensors 1009. The one or more sensors 1009 include, but are not limited to, an accelerometer 1010, a gyroscope 1011, a pressure sensor 1012, an optical sensor 1013, and a proximity sensor 1014.

[0257] Accelerometer 1010 can detect the magnitude of acceleration along the three coordinate axes of a coordinate system established by computer device 1000. For example, accelerometer 1010 can be used to detect the components of gravitational acceleration along the three coordinate axes. Processor 1001 can control display screen 1005 to display the user interface in either a landscape or portrait view based on the gravitational acceleration signal acquired by accelerometer 1010. Accelerometer 1000 can also be used for games or for acquiring user motion data.

[0258] The gyroscope sensor 1011 can detect the orientation and rotation angle of the computer device 1000. The gyroscope sensor 1011 can work in conjunction with the accelerometer sensor 1010 to acquire the user's 3D movements on the computer device 1000. Based on the data acquired by the gyroscope sensor 1011, the processor 1001 can perform the following functions: motion sensing (e.g., changing the UI based on the user's tilt), image stabilization during shooting, game control, and inertial navigation.

[0259] The pressure sensor 1012 can be disposed on the side bezel of the computer device 1000 and / or on the lower layer of the display screen 1005. When the pressure sensor 1012 is disposed on the side bezel of the computer device 1000, it can detect the user's grip signal on the computer device 1000, and the processor 1001 can perform left / right hand recognition or quick operation based on the grip signal collected by the pressure sensor 1012. When the pressure sensor 1012 is disposed on the lower layer of the display screen 1005, the processor 1001 can control the operable controls on the UI interface based on the user's pressure operation on the display screen 1005. The operable controls include at least one of button controls, scroll bar controls, icon controls, and menu controls.

[0260] An optical sensor 1013 is used to collect ambient light intensity. In one embodiment, the processor 1001 can control the display brightness of the display screen 1005 based on the ambient light intensity collected by the optical sensor 1013. For example, when the ambient light intensity is high, the display brightness of the display screen 1005 is increased; when the ambient light intensity is low, the display brightness of the display screen 1005 is decreased. In another embodiment, the processor 1001 can also dynamically adjust the shooting parameters of the camera assembly 1006 based on the ambient light intensity collected by the optical sensor 1013.

[0261] The proximity sensor 814, also known as a distance sensor, is typically located on the front panel of the computer device 1000. The proximity sensor 814 is used to detect the distance between the user and the front of the computer device 1000. In one embodiment, when the proximity sensor 814 detects that the distance between the user and the front of the computer device 1000 is gradually decreasing, the processor 1001 controls the display screen 1005 to switch from a screen-on state to a screen-off state; when the proximity sensor 814 detects that the distance between the user and the front of the computer device 1000 is gradually increasing, the processor 1001 controls the display screen 1005 to switch from a screen-off state to a screen-on state.

[0262] Those skilled in the art will understand that Figure 10 The structure shown does not constitute a limitation on the computer device 1000, and may include more or fewer components than shown, or combine certain components, or use different component arrangements.

[0263] Figure 11 This is a structural block diagram of a payment platform provided in an exemplary embodiment of this application. Figure 11 This is a schematic diagram of a server structure according to an exemplary embodiment. The server 1300 includes a Central Processing Unit (CPU) 1301, a system memory 1304 including Random Access Memory (RAM) 1302 and Read-Only Memory (ROM) 1303, and a system bus 1305 connecting the system memory 1304 and the CPU 1301. The payment platform 1300 also includes a basic input / output system (I / O system) 1306 to facilitate information transmission between various devices within the payment platform, and a large-capacity storage device 1307 for storing the operating system 1313, application programs 1314, and other program modules 1315.

[0264] The basic input / output system 1306 includes a display 1308 for displaying information and an input device 1309 for user input, such as a mouse or keyboard. Both the display 1308 and the input device 1309 are connected to the central processing unit 1301 via an input / output controller 1310 connected to the system bus 1305. The basic input / output system 1306 may also include the input / output controller 1310 for receiving and processing input from multiple other devices such as a keyboard, mouse, or electronic stylus. Similarly, the input / output controller 1310 also provides output to a display screen, printer, or other types of output devices.

[0265] The mass storage device 1307 is connected to the central processing unit 1301 via a mass storage controller (not shown) connected to the system bus 1305. The mass storage device 1307 and its associated server-readable media provide non-volatile storage for the payment platform 1300. That is, the mass storage device 1307 may include server-readable media (not shown), such as a hard disk or a compact disc read-only memory (CD-ROM) drive.

[0266] Without loss of generality, the computer device readable medium may include computer device storage media and communication media. Computer device storage media include volatile and non-volatile, removable and non-removable media implemented using any method or technology for storing information such as computer device readable instructions, data structures, program modules, or other data. Computer device storage media include RAM, ROM, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), CD-ROM, digital video disc (DVD) or other optical storage, magnetic tape cassettes, magnetic tape, disk storage, or other magnetic storage devices. Of course, those skilled in the art will recognize that the computer device storage media are not limited to the above-mentioned types. The system memory 1304 and mass storage device 1307 described above can be collectively referred to as memory.

[0267] According to various embodiments of this disclosure, the payment platform 1300 can also operate by connecting to a remote computer device on a network such as the Internet. That is, the payment platform 1300 can connect to the network 1311 via the network interface unit 1312 connected to the system bus 1305, or it can use the network interface unit 1312 to connect to other types of network or remote computer device systems (not shown).

[0268] The memory also includes one or more programs, which are stored in the memory. The central processing unit 1301 executes the one or more programs to implement all or part of the steps of the above-mentioned model training method or behavior encoding method.

[0269] This application also provides a computer-readable storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the method for identifying the operating status of a pipeline network provided in the above-described method embodiments.

[0270] This application provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method for identifying the operating status of a pipeline network provided in the above-described method embodiments.

[0271] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware, or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk. The above descriptions are merely optional embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for predicting vehicle parameters, characterized in that, The method includes: Obtain the dynamic model corresponding to the vehicle, which includes a steering model, a suspension model, and a tire model. The dynamic model is used to construct the dynamic relationship between the vehicle's driving state and physical characteristics. Based on the dynamic model, the state variables, control variables and output variables corresponding to the vehicle are obtained. The state variables include a first parameter related to the driving state and the physical characteristics. The control variables include a second parameter related to the front wheel steering angle and the suspension power. The output variables include a third parameter corresponding to the yaw angle of the vehicle and the path displacement of the vehicle in the lateral direction. Based on the state variables, the control variables, and the output variables, a baseline prediction model corresponding to the vehicle is constructed. Obtain the disturbance variables corresponding to the vehicle, wherein the disturbance variables are used to represent environmental variables that cause state disturbances to the driving state of the vehicle; Based on the state variables, control variables, output variables, and disturbance variables, a target prediction model corresponding to the vehicle is constructed. Obtain the nominal equation corresponding to the vehicle and the disturbance variable; The nominal equation is adjusted based on the disturbance variables to obtain the actual state equation; Based on the nominal equation and the actual state equation, the error state equation is determined; Determine the error prediction model corresponding to the error state equation; By coupling the error prediction model and the baseline prediction model, a vehicle parameter prediction model is obtained. The vehicle parameter prediction model is used to predict the force of the vehicle suspension when the vehicle undergoes a displacement change.

2. The method according to claim 1, characterized in that, The step of constructing a baseline prediction model for the vehicle based on the state variables, the control variables, and the output variables includes: Based on the state variables, the control variables, and the output variables, the nominal equation corresponding to the vehicle is determined, and the nominal equation is used to represent the correspondence between the state variables, the control variables, and the output variables. The nominal equation is linearized to obtain a linear nominal equation; Determine the objective function corresponding to the control variable and the output variable; Determine the minimum value corresponding to the control variable within the objective function; Based on the minimum value and the linear nominal equation, the initial prediction model is adjusted to obtain the baseline prediction model.

3. The method according to claim 2, characterized in that, The adjustment of the initial prediction model based on the minimum value and the linear nominal equation to obtain the baseline prediction model includes: Discretize the linear nominal equation to obtain the discrete state equation; Perform a rolling prediction process on the discrete state equations to obtain rolling state equations; Based on the minimum value and the rolling state equation, the initial prediction model is adjusted to obtain the baseline prediction model.

4. The method according to claim 3, characterized in that, Determining the objective function corresponding to the control variable and the output variable includes: The ideal path displacement of the vehicle is determined based on the initial prediction model. The objective function is determined based on the ideal path displacement and control variables.

5. The method according to claim 2, characterized in that, Determining the minimum value corresponding to the control variable within the objective function includes: Determine the constraint conditions corresponding to the front wheel steering angle, wherein the constraint conditions are used to indicate the value range of the front wheel steering angle; Based on the constraints, the minimum value corresponding to the control variable within the objective function is determined using a preset solution method.

6. The method according to claim 1, characterized in that, The process of coupling the error prediction model and the baseline prediction model to obtain the vehicle parameter prediction model includes: The minimum value of each output variable under the error nominal state system is solved using a preset algorithm. The error prediction model is trained based on the minimum value to obtain the target error prediction model; The vehicle parameter prediction model is obtained by coupling the target error prediction model and the baseline prediction model.

7. A vehicle parameter prediction device, characterized in that, The device includes: The acquisition module is used to acquire the dynamic model corresponding to the vehicle. The dynamic model includes a steering model, a suspension model, and a tire model. The dynamic model is used to construct the dynamic relationship between the vehicle's driving state and physical characteristics. The acquisition module is further configured to acquire the state variables, control variables and output variables corresponding to the vehicle based on the dynamic model. The state variables include a first parameter related to the driving state and the physical characteristics. The control variables include a second parameter related to the front wheel steering angle and the suspension power. The output variables include a third parameter corresponding to the yaw angle of the vehicle and the path displacement of the vehicle in the lateral direction. A construction module is used to construct a baseline prediction model for the vehicle based on the state variables, the control variables, and the output variables. The acquisition module is also used to acquire the disturbance variable corresponding to the vehicle, and the disturbance variable is used to represent the environmental variable that causes state disturbance to the driving state of the vehicle. The construction module is used to construct a target prediction model corresponding to the vehicle based on the state variable, the control variable, the output variable, and the disturbance variable. The acquisition module is also used to acquire the nominal equation corresponding to the vehicle and the disturbance variable; The determination module is used to adjust the nominal equation based on the disturbance variable to obtain the actual state equation; determine the error state equation based on the nominal equation and the actual state equation; and determine the error prediction model corresponding to the error state equation. A coupling module is used to couple the error prediction model and the baseline prediction model to obtain a vehicle parameter prediction model, which is used to predict the force of the vehicle suspension when the vehicle undergoes displacement changes.

8. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one program that is loaded and executed by the processor to implement the vehicle parameter prediction method as described in any one of claims 1 to 6.

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